ALGORITHMIC DETERMINISM VERSUS HUMAN AGENCY: A SYSTEMATIC REVIEW AND META-ANALYSIS OF ARTIFICIAL INTELLIGENCE AND HR ANALYTICS IN ORGANIZATIONAL DECISION-MAKING

Abstract

The emergence of Artificial Intelligence (AI) and HR Analytics has transformed the epistemology and practice of organizational decision making. In this paper, we conduct one of the most thorough systematic reviews and meta-analysis to empirically explore the impact of data-driven technology on decision quality, organizational performance, and employee outcomes. Utilizing 85 publications and theories of algorithm-automated decision-making (AST) and matching/hybrid models (STS), we analyze the algorithm-automated vs. human decision debate. The meta-analysis reveals a small to moderate direct positive relationship between AI use and operational productivity (r = 0.28, I^2 = 74%). Most moderators have a considerable influence. Data maturity, ethical governance of algorithms, and industry type shape business performance in AI-augmented workflows. In addition, qualitative synthesis shows a 'gray zone' in labor relations and a 'black box' in algorithmic data processing that both expose businesses to procedural injustice risks. Our findings suggest that while AI has a potential to bring predictive benefits for recruitment and retention, it poses risks of systemic discrimination, privacy invasion, and commodification of talent. To reduce this duality, the paper proposes a dynamic Human-in-the-Loop model that reconciles the deterministic nature of algorithms with the normative demands of human resource management.